Name
Abstract | ||
|---|---|---|
This paper puts forward a comprehensive study of the design of global stopping criteria for multi-objective optimization. In this study we propose a global stopping criterion, which is terms as MGBM after the authors surnames. MGBM combines a novel progress indicator, called mutual domination rate (MDR) indicator, with a simplified Kalman filter, which is used for evidence-gathering purposes. The MDR indicator, which is also introduced, is a special-purpose progress indicator designed for the purpose of stopping a multi-objective optimization. As part of the paper we describe the criterion from a theoretical perspective and examine its performance on a number of test problems. We also compare this method with similar approaches to the issue. The results of these experiments suggest that MGBM is a valid and accurate approach. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.ins.2016.07.025 | Inf. Sci. |
Keywords | Field | DocType |
Stopping criteria,Convergence detection,Stagnation,Progress indicators,Multi-objective evolutionary algorithms,Multi-objective optimization,Kalman filters | Mathematical optimization,Evolutionary algorithm,Kalman filter,Multi-objective optimization,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
367-368 | C | 0020-0255 |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis Martí | 1 | 100 | 7.73 |
| Jesús Garcia | 2 | 12 | 3.40 |
| Antonio Berlanga | 3 | 196 | 23.09 |
| Jose M. Molina | 4 | 118 | 31.45 |